NSF-BSF: Efficiently Modeling Continuous Quantum Measurements of High-Dimensional Multi-Qubit Systems

NSF-BSF：高维多量子位系统的连续量子测量的高效建模

• 批准号: 1915015
• 负责人: Justin Dressel
• 金额: $ 32.1万
• 依托单位: Chapman University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915015&HistoricalAwards=false
• 关键词: NSF; BSF; Efficiently; Modeling; Continuous

Modern computers developed rapidly, leading to a historically unprecedented wealth of technology. This technological revolution has improved standards of living globally and has become a cornerstone of the modern economy. Recently, the rapid growth of computational power has slowed, in part because the size of hardware components has shrunk to microscopic scales. At microscopic scales, hardware behaves according to the laws of quantum mechanics, which are quite different from the laws expected for traditional computers. These differences have impeded continued growth using established hardware techniques, but also allow for new possibilities. Efforts are ongoing to develop a paradigm of hardware that leverages the nuances of quantum mechanics to accelerate computation. This project contributes to these quantum computing efforts by addressing a pressing simulation problem for superconducting quantum circuits, which are a promising candidate for scalable quantum technology. The difficulty in accurately describing such a quantum circuit grows rapidly with the size of the system, making hardware design challenging. If successful, this work will provide numerical methods and open source software that dramatically simplify this modeling task for common scenarios, which should help accelerate the future development of superconducting quantum circuits.Large numbers of parameters are generally required to describe quantum circuits, making brute force simulation challenging. A particularly important example of this high dimensionality occurs during the standard measurement protocol for quantum circuits. In this protocol, traveling microwave fields couple with microwave resonators, which in turn couple with nonlinear oscillators that have several energy levels. As the traveling field is collected, the quantum system continuously evolves in accordance with the measured stochastic signal, producing complicated dynamics. This project will develop efficient methods for simulating these continuous quantum measurements using several design phases. After developing a full reference numerical model for the microwave amplification and readout circuitry on a multi-component chip, we will develop simplified semi-classical representations that compress the high dimensionality into a smaller number of parameters. These simplifications will extend known weak-field coherent steady-state approximations of the microwave dynamics to account for nonlinear effects. This project will explore the use of machine learning methods, particularly recurrent neural networks, to automatically learn how to compress the dynamics efficiently. In parallel, undergraduates will perform outreach to the local community through demonstrations, videos, and more to raise public literacy of quantum mechanics. This project will deliver open-source software, online interactive notes, and tutorials as part of its broad outreach effort.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代计算机迅速发展，导致历史上史无前例的技术财富。这场技术革命改善了全球生活水平，并已成为现代经济的基石。最近，计算能力的快速增长已经减慢，部分原因是硬件组件的大小已缩小到微观尺度。在微观尺度上，硬件根据量子力学定律的行为，这与传统计算机的预期定律完全不同。这些差异使用已建立的硬件技术阻碍了持续的增长，但也允许新的可能性。正在进行努力开发硬件范式，以利用量子力学的细微差别加速计算。该项目通过解决超导量子电路的压迫模拟问题来促进这些量子计算工作，这是可扩展量子技术的有前途的候选人。准确地描述这种量子电路的困难随着系统的大小而迅速增长，使硬件设计具有挑战性。如果成功的话，这项工作将提供数值方法和开源软件，这些软件大大简化了常见场景的建模任务，这将有助于加速超导量子电路的未来开发。通常需要大量参数来描述量子电路，从而使野蛮的力量模拟挑战。这种高维度的一个特别重要的例子发生在量子电路的标准测量协议中。在此协议中，旅行的微波炉夫妇与微波谐振器，这些偶数依次与具有多个能级的非线性振荡器。当收集行进场时，量子系统会根据测得的随机信号连续发展，从而产生复杂的动力学。该项目将开发有效的方法，用于使用几个设计阶段模拟这些连续的量子测量。在为多组分芯片上开发了用于微波放大和读数电路的完整参考数值模型之后，我们将开发简化的半经典表示，将高维度压缩为较小数量的参数。这些简化将扩展微波动力学的已知弱场相干稳态近似值，以解释非线性效应。该项目将探讨机器学习方法，尤其是经常性神经网络的使用，以自动学习如何有效地压缩动态。同时，本科生将通过演示，视频等向当地社区进行宣传，以提高量子力学的公共素养。该项目将提供开源软件，在线交互式注释和教程，这是其广泛的外展工作的一部分。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。

项目成果

Counterfactual communication without a trace in the transmission channel

• DOI: 10.1038/s41534-023-00756-y
• 发表时间: 2023-08
• 期刊: npj Quantum Information
• 影响因子: 7.6
• 作者: W. Pan;Xiao Liu;Xiao-Ye Xu;Qin-Qin Wang-Qin;Ze-Di Cheng;Jian Wang;Zhao-Di Liu;Geng Chen;Zong-Quan Zhou;Chuan‐Feng Li;G. Guo;J. Dressel;L. Vaidman

Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning

使用机器学习的超导处理器的量子态估计和跟踪

• 发表时间: 2021
• 期刊: Ph.D. Thesis
• 作者: Barzili, Shiva L.

Failed attempt to escape from the quantum pigeon conundrum

逃离量子鸽子难题的尝试失败

• DOI: 10.1016/j.physleta.2021.127287
• 发表时间: 2021
• 期刊: Physics Letters A
• 影响因子: 2.6
• 作者: Aharonov, Yakir;Bagchi, Shrobona;Dressel, Justin;Reznik, Gregory;Ridley, Michael;Vaidman, Lev
• 通讯作者: Vaidman, Lev

Footprints of quantum pigeons

• DOI: 10.1103/physrevresearch.2.023004
• 发表时间: 2020-02
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Gregory Reznik;Shrobona Bagchi;J. Dressel;L. Vaidman

Monitoring Fast Superconducting Qubit Dynamics Using a Neural Network

使用神经网络监控快速超导量子位动态

• DOI: 10.1103/physrevx.12.031017
• 发表时间: 2022
• 期刊: Physical Review X
• 影响因子: 12.5
• 作者: Koolstra, G.;Stevenson, N.;Barzili, S.;Burns, L.;Siva, K.;Greenfield, S.;Livingston, W.;Hashim, A.;Naik, R. K.;Kreikebaum, J. M.
• 通讯作者: Kreikebaum, J. M.